A subjective analysis of stained tissue sections is a critical step in the detection and diagnosis of most cancer in developed countries. To help differentiate cancerous from normal tissue in these decisions, stereological parameters exist to quantify mean nuclear size and 3-D patterns of clustering and anisotropy. In manual (non-automated) form, however, these labor-intensive and tedious methods are prohibitive for broad clinical applications. Automatic quantification of these parameters requires segmentation, which is complicated by variations in the staining characteristics of cancerous and normal tissue, as well as within and between sections from the same tissue.
Accordingly, there is a need in the art for a system and method that reduces the time and cost for a trained expert to manually identify possible cases of cervical and other forms of cancer based on stained tissue sections from Pap smears and cervical biopsies.